Tag Archives: demography

Immigrant health paradox update

I wrote a few years ago about the surprisingly low infant mortality rates among immigrants, especially Mexican immigrants, given their relative socioeconomic status. As poor as they other, in other words, we would expect higher infant mortality rates than they have. This has been called the epidemiological paradox. Here is an update, which includes some text from the previous post.

In almost every race/ethnic group, immigrants are healthier.* Here’s the pattern for infant mortality, now updated with 2010 infant mortality rates from federal vital statistics records (click to enlarge).

epipara

For Latinos in particular, their health is surprisingly good given their economic conditions. Robert Hummer and colleagues, in a 2007 article, offered a succinct description:

…the relatively low levels of education, income, and health insurance coverage among Hispanics compared with non-Hispanic whites is thought to place the former at higher risk for negative health outcomes. However, it is well documented that some Hispanic groups exhibit similar observed death rates compared with the non-Hispanic white population and much lower death rates than the non-Hispanic black population, whom they closely resemble with respect to socioeconomic characteristics. The greatest enigma is exhibited by the Mexican-origin population of the United States. This Hispanic subgroup is characterized by low educational attainment; low health insurance coverage rates; mortality rates similar to non-Hispanic whites; and much more favorable mortality rates than those of non-Hispanic blacks across most of the life course.

In a 2013 revisiting of the paradox, Daniel Powers confirms the basic pattern, but adds an important wrinkle for Mexican mothers: the foreign-born advantage disappears for older mothers. Thus, children born to older Mexican immigrants have similar risks as those who mothers are born in the U.S. He concludes, in part:

Given the association between infant survival and maternal health, differential infant survival within the Mexican-origin population suggests that longer exposure to social conditions in the U.S. undermines the health of mothers who, in general, seem to have more favorable health endowments than their non-Hispanic white counterparts as evidenced by the relatively lower rates of infant mortality at younger ages.

Immigrants are often healthier than the average people in the countries they came from, which explains some of the paradox. However, our ability to accurately assess the relative health of immigrants versus the populations they left behind is limited by available data. Further, in the case of Mexico, the situation is complicated by cyclical movements of immigration and emigration. In a recent paper, Georgiana Bostean reviews this problem, and compares the health of immigrants, non-migrants, and return migrants to Mexico. And — It’s complicated. She concludes:

…there is no simple explanation for Latinos’ perplexing health outcomes, such as simply that healthier people migrate. Rather, migrants are positively selected in some health aspects, negatively selected in others, and in yet other health outcomes, there is no selection effect. In sum, selective migration plays a role in explaining some of U.S. Latinos’ health outcomes, but is not the only explanation and does not account for the Paradox.

These articles are a good place to start on this topic: lots of references to fill in the background and previous research on this paradox, which goes back at least to the 1980s. This is a fascinating and important research area, dealing with such questions as health behaviorintergenerational change, thorny puzzles about different immigrant groups, child development and lots more.

*Because Puerto Rico is part of the U.S. (albeit not a free part), people born in Puerto Rico who move to the states are not immigrants, just migrants. In the figure I used the terms “US Born” and “Foreign born,” but this is just shorthand, and not strictly accurate for Puerto Ricans.

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Marriage, divorce, remarriage, age, education (Coontz tabs edition)

Stephanie Coontz has an excellent Op-Ed on the front of today’s New York Times Sunday Review, which draws out the implications for family instability of the connection between increasing gender equality on the one hand, and increasing economic inequality and insecurity on the other. The new instability is disproportionately concentrated among the population with less than a college degree. To help with her research, I gave Stephanie the figure below, but it didn’t make the final cut. This shows the marriage history of men and women by education and age. She wrote:

According to the sociologist Philip N. Cohen, among 40-somethings with at least a bachelor’s degree, as of 2012, 63 percent of men and 59 percent of women were in their first marriage, compared to just 43 percent of men and 42 percent of women without a bachelor’s degree.

I highlighted those numbers in the figure. Also striking is the higher percentage of divorced people among those with less than a BA degree (and higher widowhood rates). Click to enlarge: age marriage history Cross-posted on the Families As They Really Are blog.

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Border fences make unequal neighbors

 

israelgazafence

 

There is one similarity between the Israel/Gaza crisis and the U.S. unaccompanied child immigrant crisis: National borders enforcing social inequality. When unequal populations are separated, the disparity creates social pressure at the border. The stronger the pressure, the greater the military force needed to maintain the separation.

To get a conservative estimate of the pressure at the Israel/Gaza border, I compared some numbers for Israel versus Gaza and the West Bank combined, from the World Bank (here’s a recent rundown of living conditions in Gaza specifically). I call that conservative because things are worse in Gaza than in the West Bank.

Then, just as demographic wishful thinking, I calculated what the single-state solution would look like on the day you opened the borders between Israel, the West Bank, and Gaza. I added country percentiles showing how each state ranks on the world scale (click to enlarge).

israelwbgaza

Israel’s per capita income is 6.2-times greater, its life expectancy is 6 years longer, its fertility rate is a quarter lower, and its age structure is reversed. Together, the Palestinian territories have a little more than half the Israeli population (living on less than 30% of the land). That means that combining them all into one country would move both populations’ averages a lot. For example, the new country would be substantially poorer (29% poorer) and younger than Israel, while increasing the national income of Palestinians by 444%. Israelis would fall from the 17th percentile worldwide in income, and the Palestinians would rise from the 69th, to meet at the 25th percentile.

Clearly, the separation keeps poor people away from rich people. Whether it increases or decreases conflict is a matter of debate.

Meanwhile

Meanwhile, the USA has its own enforced exclusion of poor people.

Photo of US/Tijuana border by Kordian from Flickr Creative Commons

Photo of US/Tijuana border by Kordian from Flickr Creative Commons.

The current crisis at the southern border of the USA mostly involves children from Guatemala, Honduras, and El Salvador. They don’t actually share a border with the USA, of course, but their region does, and crossing into Mexico seems pretty easy, so it’s the same idea.

To make a parallel comparison to Israel and the West Bank/Gaza, I just used Guatemala, which is larger by population than Honduras and El Salvador combined, and also closest to the USA. The economic gap between the USA and Guatemala is even larger than the Israeli/Palestinian gap. However, because the USA is 21-times larger than Guatemala by population, we could easily absorb the entire Guatemalan population without much damaging our national averages. Per capita income in the USA, for example, would fall only 4%, while rising more than 7-times for Guatemala (click to enlarge):

guatemalausa

This simplistic analysis yields a straightforward hypothesis: violence and military force at national borders rises as the income disparity across the border increases. Maybe someone has already tested that.

The demographic solution is obvious: open the borders, release the pressure, and devote resources to improving quality of life and social harmony instead of enforcing inequality. You’re welcome!

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What a recovery looks like (with population growth by age)

If you don’t account for population growth, I don’t get what you’re saying with these employment numbers. I’ll show a simple example, but first a little rundown on Friday’s jobs report.

Here is how CNN Money played the jobs report:

cnn-jobs

What does it mean, this loss and gain of jobs, returning finally to where we started? Four paragraphs under that happy headline, CNN did points out:

Given population growth over the last four years, the economy still needs more jobs to truly return to a healthy place. How many more? A whopping 7 million, calculates Heidi Shierholz, an economist with the Economic Policy Institute.

So what does “Finally!” mean? The Wall Street Journal ran the headline, “Jobs Return to Peak, but Quality Lags.” On 538 it was, “Women returned to prerecession levels of employment in 2013. Men remain hundreds of thousands of jobs in the hole:”

538-jobs

The Center on Budget and Policy Priorities showed how much better the previous recoveries were:

cbpp-jobs

That’s a good comparison. And CBPP mentioned population growth, too:

…payroll employment has finally topped its level at the start of the recession. Still, with essentially no net job growth since December 2007 but a growing working-age population, many more people today want to work but don’t have a job.

It’s not that no one mentions population growth, it’s that they still lead with the “top line” number. And they all have that horizontal line at the raw number of jobs when the recession started as the benchmark. I don’t know why.

Maybe in some crazy economics world the absolute number of jobs is what really matters for evaluating a recovery, and that explains the fixation on that horizontal line. From a social perspective what matters is the proportion of people with jobs. I could see the logic if you had a finite number of employers that never change, where you could literally count up the jobs at two points in time, and see who added and who subtracted from their payrolls (this is why retail chains report “same-store” trends, so the statistics aren’t confounded by the changing number of stores). But we have zillions of employers, constantly changing and moving around — largely in response to population changes. So that static image seems pointless.

In perspective

So here are some charts to put the recession and recovery in slightly better perspective. These all use the Bureau of Labor Statistics’ Current Population Survey from March 2003 to March 2013 (from IPUMS), the household survey used to track the labor force. I use ages 15 and older, and combine people in school (up to age 24) with those employed (not how most people do it, but a lot of people went to school, or stayed in school, because of the bad job market, and it doesn’t make sense to count them as not simply not employed). The survey excludes people in institutions, like prisons, and on-base military personnel.

To show the basic issue, here are the changes in the non-institutionalized population, age 15+, along with the number of them employed or in school — showing absolute changes relative to 2008, the peak employment year.

popjobs1

The 15+ population increased almost 12 million from 2008 to 2013. People employed or in school was not yet back to 2008 levels in March 2013. So a basic population adjustment is the least you can ask for (and we get that from the BLS with the employment-population ratio, which as of May was up less than one percent in the last 3.5 years, but it’s not the headline number).

What about age shifts? You don’t expect extreme age composition changes in 5 years, but there are different employment trends at different ages, so those affect how many employed people we are short. Here are the trends in work/school, by age and sex:

popjobs2

This makes it look like the 30-49s that are getting crushed. The 50+ community’s gains, however,are deceptive — their population is increasing. In fact, the population of people 30-49 declined 5% during this decade, while the population 50+ increased almost 30%. The younger people have increased their schooling rates, but their population has also grown. If you look at the employment/school rates, you see that among men, it is the younger groups that have done worst:

popjobs3

Women clearly are doing better (partly because in the 20-29 range they’re going to school more). It is amazing that employment rates didn’t fall at all over age 60. This could be because the population increase in that group is all in Baby Boomers just hitting their sixties, but I reckon it’s also people delaying retirement compensating for unemployment.

Now that we have age-specific work/school rates, and population changes, we can easily calculate how many people in each age group would have to be in work/school to get to the number implied by applying the peak-year 2008 rates to the population in each year. Sorry this one is so ugly: I made the last bar for each group pink to show the bottom line, where each group stands in 2013 relative to 2008:

popjobs4

Worst off are the 20-something men, down more than a million worker/students in 2013. Interestingly, women are only better off in the 20-something and 50+ ranges.

Finally, if you sum these figures you get the total, age-adjusted losses, by sex since 2008, as of March 2013:

popjobs5

And that’s your bottom line. The job/school losses stood at 3.3 million for men and 2.4 million for women as of March 2013.*

Really, there are no huge surprises here. In fact, the total population change is not a bad rough adjustment, especially for the short term. But there are some interesting nuances here. And with all the data and computers we have these days, let’s adjust for age and sex.

*I don’t say that’s how many “jobs” we need, because I don’t think “jobs” exist — are created, destroyed, shipped overseas, etc. I think there are employed people, people getting jobs, losing jobs, etc. I don’t see how a “job” exists absent a worker in it (and no, a listing is not a job until they fill it). So we don’t need to “create jobs” after a recession, what we need to do is “hire people.” Pet peeve.

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Changing Hispanic racial identity, or not

Hector Cordero-Guzman called my attention to a controversy over Hispanics changing their racial identities. Here is a quick rehash and a few comments. (Spoiler: the New York Times ran a bad story.)

At the Population Association of America, Carolyn Liebler, a sociologist at the University of Minnesota, and James Noon, who works on administrative records at the Census Bureau, presented preliminary results from a comparison of individual race/ethnic responses to the 2000 and 2010 Decennial Censuses. After analyzing millions of individual Census responses, they reported in their abstract that 6% of people changed their race or Hispanic origin classification between 2000 and 2010.

Details of the analysis apparently are not publicly available, but D’Vera Cohn, a writer at the Pew Research Center, reported on their findings, under the headline, “Millions of Americans changed their racial or ethnic identity from one census to the next.” Is this a lot of change? It’s hard to say without a comparison (and without the analysis details). “Millions” does not really mean “a lot,” but it sounds like it does. If the Census race/ethnic identity questions don’t fit people’s self-concept very well then a certain amount of bouncing around is to be expected.

The focus was on Hispanics, whose place in the racial classification scheme is squishy (including immigrants who came at different ages from countries with different racial schemes and ancestral origins, living in different parts of the country with different racial attitudes, some concentrated in dense communities and some dispersed, some economically marginalized and some much more upwardly mobile, etc.). According to D’vera Cohn, 2.5 million Hispanics were “some other race” in 2000 and “white” in 2010, while 1.3 million were “white” in 2000 and “some other race” in 2010.

I might conclude from that that it’s messy and the categories don’t work very well. But it’s also possible that this reflects fluid identities, and people actually change how they see themselves in a systematic way over time. The PAA abstract says “responses and corresponding identities can change over time,” which leaves open the possibility that the change is in measurement in addition to identity, but the hypothesis they suggest are about identity (hypothesizing that women, young people, and people in the West have more complex or less stable identities).

Identity shift is how New York Times Upshot writer Nate Cohn interpreted the D’Vera Cohn report. Under the headline, “More Hispanics Declaring Themselves White,” he converted that bidirectional flow into “net 1.2 million” changing from “some other race” to “white,” and proceeded to run away with the implications. It’s a good example of using any number greater than zero to confirm something you already believe. For example, he wrote:

The data also call into question whether America is destined to become a so-called minority-majority nation, where whites represent a minority of the nation’s population. Those projections assume that Hispanics aren’t white, but if Hispanics ultimately identify as white Americans, then whites will remain the majority for the foreseeable future.

Hm. The “net” flow from “some other race” to “white” is 1.2 million. That is 3% of the 2000 Hispanic population, or 2% of the 2010 population. So even if it’s truly an identity change, does that save the White majority in the long run?

Anyway, as Cordero-Guzman points out in a detailed discussion, referring to a post by Manuel Pastor, the Census questions changed between 2000 and 2010, with Census adding, in bold, “For this census, Hispanic origins are not races” to the form in 2010. Since many Hispanics write “Hispanic” under “some other race,” this probably discouraged them from choosing “some other race” in 2010.

Cordero-Guzman also points out that the context in which the question is asked (and in which the respondents live) is important. For example, 82% of Puerto Ricans on the island use “white” on the American Community Survey, while in New York City only 45% do. Does their identity — in the sense of how they really think of themselves — change when they are in New York, or do they interpret the question differently because they are answering a question in a different social setting? You can’t quantify that difference, probably, but I wouldn’t assume it’s just an identity change.

In a follow-up post, Nate Cohn acknowledges the wording changes — “an important detail” — but returns to the assimilation-upward mobility story. He should have just acknowledged that he jumped to conclusions in the first post and overreached in the race to produce an important, “data-driven” post. (Nate Cohn may have consulted actual experts, but if he did he didn’t include them in the post. It’s just data, I guess.)

The information economy did it

There is a lesson here in the new information economy. Academic conferences used to be less in the public eye. A preliminary analysis, shared with other researchers, then a Pew writer posts on the results, and the Times splashes them all over, all before a paper is even available. I think the Times story is basically wrong — the data as reported are not independent evidence of “assimilation.” (So, the person with the biggest megaphone was the person who was most wrong — surprise!) But the analysis could well be an important piece of research in a larger literature, and I think it’s good to present preliminary research at conferences. You can’t stop reporters from racing to be wrong, but I do think it would be better to distribute the paper publicly when it’s presented.

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US teen birth rates remain high, and they’re not falling for the reasons you’ve heard

Everyone is excited by the decline in the teen birth rate in the US. But And here are a few things you should know about it.

This chart shows the birth rates for women ages 15 to 19 in 192 countries, plus the world and the UN-defined rich countries, for 1991 and 2011. Dots below the black line show countries where the teen birth rate fell. The red line shows the overall relationship between 1991 and 2011. Dots below the red line had greater than expected reduction in teen births.

teen births global

Source: My graph United Nations data.

The chart shows four things:

1. Teen birth rates are falling globally. From 1991 to 2011, the birth rate for women ages 15 to 19 fell from 65 to 46 births per 1,000 women worldwide.

2. US has higher teen birth rates than any other rich country. At 33 per 1,000, the US has more teen births than Pakistan (28), but fewer than India (36). For high income countries, by the UN definition, the rate is 19. The rate for the Euro area is 7.

3. The teen birth rate is falling faster in the US than in the world overall. The world rate fell 29% from 1991 to 2011, while the drop in the US was 44%.

In the US, there are a lot of factors related to falling teen births. But they’re mostly about how it’s happening, not why it’s happening. For example, Vox published a list of factors, as did Pew before them, that are reasonable: the recession, more birth control, more Medicaid money for family planning, cultural pressure, and less sex.

But to understand why this is happening, you have to stop thinking about teenagers as some sort of separate subspecies. They are just young women. Soon they will be in their 20s. The same women! So the short answer for why falling teen birth rates happening is this:

4. Teen birth rates in the US are falling because women are postponing their births generally.

You can see this if you line up teens next to women of other ages. Here are the changes in birth rates for women, by age, from 1989 to 2012.

birthratechangebyage

Source: My graph from National Center for Health Statistics data.

See how the trend for the last decade is parallel for 15-17, 18-19, and 20-24? As those rates fell, birth rates rose for the 30+ community. The younger women are, the fewer births they’re having; the older they are, the more births they’re having. Teenage women are women! They do it for all the reasons it’s happening around the world: some because they are delaying marriage, some to pursue education and careers, some to see the world, and so on.

Here is another way to look at this. Here are the 50 US states, from the 2000-2012 American Community Survey. This shows that states with lower teen birth rates (those are per 100, on the y-axis), have higher birth rates for 25-34 year-old women relative to 20-24 year-old women. I’ll explain:

teenbirthstates

Teen births rates and the ratio of teen birth rates ages 25-34 / 20-24. US states, 2010-2011

Where more women have children ages 25-34 relative to 20-24, there are fewer teen births. So, in Alabama, about 3% of women 15-19 had a baby per year, and in that state the birth rates are about the same for women 25-34 as 20-24. Alabama is an early-birth state. But in New Hampshire, only 1% of teens had a baby, and women 25-34 were almost 2.5-times more likely to have a baby than women 20-24. New Hampshire is a late-birth state. What’s happening with teens reflects what’s happening with older women.

To some significant degree, it’s not about teenagers, it’s about women delaying births.* I would love it if reporting on teen births would always compare them to older women.

*Notice I didn’t just exaggerate and say, “it’s not about teenagers.” I added “to some significant degree.” That’s the difference between a post that is selling you (your clicks) to someone versus a post that’s trying to explain things as clearly as possible.

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Check that: Most marrying people are remarrying above age 31

The other day I wrote that the majority of people marrying over age 35 have been married before. That is true, but because of the way I handled the age categories it’s not specific enough. In fact, the majority of men marrying over age 30, and the majority of women marrying over age 28, have been married before.

Here are the details, in two charts, both using marital events data from the 2012 American Community Survey from IPUMS.org. The first shows the breakdown between first-married and previously-married people marrying at each age. It is not until age 40 for men, and age 38 for women, that previously-married people become the majority marrying at each age. These proportions reach two thirds in the mid-40s and surpass 80% by age 52:

timesmarriedmarrying-area

But the percent remarrying at or above a given age is higher. Here is that pattern, showing that we enter majority-remarried territory at 31 for men and 29 for women:

timesmarriedmarrying-lines

The rates of remarriage at a given age maybe matter more practically, but this is a neat way to look at it.

Note there is no demographic reason that these patterns must hold. If remarriage were taboo or more restricted this would not be the case. Being ever married cannot be revoked (unless people lie to the Census Bureau), so the percent ever-married should never decline for a cohort (unless the ever-married have much higher mortality or emigrate more than the never-married, which is very unlikely). But ever-married proportions for the population don’t have to rise with age in a given cross-section, even if you don’t just look at people marrying right now. If marriage were becoming more common on a cohort basis, for example (which it is not), you could see higher ever-married rates among young people than among old people.

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